Principles for Building Biomedical Ontologies ISMB 2005 November 14, 2018

Introductions Suzanna Lewis: Michael Ashburner: Barry Smith: Head of the BDGP bioinformatics group and a founder of the GO Michael Ashburner: Professor of Biology at the University of Cambridge; Founder and PI of FlyBase; and Founder and PI of the GO Barry Smith: Research Director of the ECOR Rama Balakrishnan: Scientific Content Editor at the SGD and for the GO David Hill: Scientific Content Editor at the MGI and for the GO Mark Musen: Head of Stanford Medical Informatics BDGP == Berkeley Drosophila Genome Project GO == Gene Ontology ECOR == European Center for Ontological Research SGD == Saccharomyces Genome Database MGI == Mouse Genome Informatics Michael also was Former Joint Head of the EBI November 14, 2018

Special thanks to Christopher J. Mungall Winston Hide November 14, 2018

Outline for the Afternoon A definition of “ontology” Four sessions: Organizational Management Principles for Ontology Construction Case Studies from the GO Debate of critical issues. November 14, 2018

Ontology (as a branch of philosophy) The science of what is: of the kinds and structures of the objects, and their properties and relations in every area of reality. In simple terms, it seeks the classification of entities and the relations between them. Defined by a scientific field's vocabulary and by the canonical formulations of its theories. Seeks to solve problems which arise in these domains. November 14, 2018

In computer science, there is an information handling problem Different groups of data-gatherers develop their own idiosyncratic terms and concepts in terms of which they represent information. To put this information together, methods must be found to resolve terminological and conceptual incompatibilities. Again, and again, and again… November 14, 2018

The Solution to this Tower of Babel problem A shared, common, backbone taxonomy of relevant entities, and the relationships between them, within an application domain This is referred to by information scientists as an ’Ontology'. Provide significant advantages over the case-by-case resolution of incompatibilities. November 14, 2018

Which means… Instances are not included! It is the generalizations that are important (…though instances must be taken into account) Please keep this in mind, it is a crucial to understanding the tutorial November 14, 2018

Motivation: to capture biology. Inferences and decisions we make are based upon what we know of the biological reality. An ontology is a computable representation of this underlying biological reality. Enables a computer to reason over the data in (some of) the ways that we do. Presumably November 14, 2018

Concept Concepts are in your head and will change as our understanding changes Universals exist and have an objective reality November 14, 2018

Organization Challenges for Building Biomedical Ontologies Michael Ashburner and Suzanna Lewis http://obo.sourceforge.net November 14, 2018

Collaborate & Learn (Listen to Barry) Why Survey Domain covered? Public? Community? Active? Salvage Develop Applied? Improve yes no Collaborate & Learn (Listen to Barry) November 14, 2018

Evaluating ontologies What domain does it cover? It is privately held? Is it active? Is it applied? November 14, 2018

Collaborate & Learn (Listen to Barry) Survey Why Domain covered? Public? Community? Active? Salvage Develop Applied? Improve yes no Collaborate & Learn (Listen to Barry) November 14, 2018

Due diligence & background research Step 1: Learn what is out there The most comprehensive list is on the OBO site. http://obo.sourceforge.net Assess ontologies critically and realistically. November 14, 2018

Collaborate & Learn (Listen to Barry) Why Survey Domain covered? Public? Community? Active? Salvage Develop Applied? Improve yes no Collaborate & Learn (Listen to Barry) November 14, 2018

Ontologies must be shared Proprietary ontologies Belief that ownership of the terminology gives the owners a competitive edge For example, Incyte or Monsanto in the past November 14, 2018

Ontologies must be shared Communities form scientific theories that seek to explain all of the existing evidence and can be used for prediction These communities are all directed to the same biological reality, but have their own perspective The computable representation must be shared Ontology development is inherently collaborative Open ontologies become connected to instance data & this feeds back on ontology development November 14, 2018

Collaborate & Learn (Listen to Barry) Why Survey Domain covered? Public? Community? Active? Salvage Develop Applied? Improve yes no Collaborate & Learn (Listen to Barry) November 14, 2018

Pragmatic assessment of an ontology Is there access to help, e.g.: help-me@weird.ontology.net ? Does a warm body answer help mail within a ‘reasonable’ time—say 2 working days ? November 14, 2018

Collaborate & Learn (Listen to Barry) Why Survey Domain covered? Public? Community? Active? Salvage Develop Applied? Improve yes no Collaborate & Learn (Listen to Barry) November 14, 2018

Where the rubber meets the road Every ontology improves when it is applied to actual instances of data It improves even more when these data are used to answer research questions There will be fewer problems in the ontology and more commitment to fixing remaining problems when important research data is involved that scientists depend upon Be very wary of ontologies that have never been applied November 14, 2018

Work with that community To improve (if you found one) To develop (if you did not) How? Improve Collaborate and Learn November 14, 2018

Experience from building the GO A little sociology Experience from building the GO November 14, 2018

Community vs. Committee ? Members of a committee represent themselves. Committees design camels Members of a community represent their community. Communities design race horses Many people have (understandably) reservations about collaborative development because it can easily be confused with design-by-committee projects. Ontology development is inherently collaborative, and so it is important to understand the difference November 14, 2018

Design for purpose - not in abstract Who will use it? If no one is interested, then go back to bed What will they use it for? Define the domain Who will maintain it? Be pragmatic and modest November 14, 2018

Start with a concrete proposal —not a blank slate. But do not commit your ego to it. Distribute to a small group you respect: With a shared commitment. With broad domain knowledge. Who will engage in vigorous debate without engaging their egos (or, at least not too much). Who will do concrete work. November 14, 2018

Step 1: Alpha0: the first proposal - broad in breadth but shallow in depth. By one person with broad domain knowledge. Distribute to a small group (<6). Get together for two days and engage in vigorous discussion. Be open and frank. Argue, but do not be dogmatic. Reiterate over a period of months. Do as much as possible face-to-face, rather than by phone/email. Meet for 2 days every 3 months or so. November 14, 2018

Step 2: Distribute Alpha1 to your group. All now test this Alpha1 in real life. Do not worry that (at this stage) you do not have tools - hack it. November 14, 2018

Step 3: Reconvene as a group for two days. Share experiences from implementation: Can your Alpha1 be implemented in a useful way ? What are the conceptual problems ? What are the structural problems ? November 14, 2018

Step 4: Establish a mechanism for change. Release a Beta1. Use CVS or Subversion. Limit the number of editors with write permission (ideally to one person). Release a Beta1. Seriously implement Beta1 in real life. Build the ontology in depth. November 14, 2018

Step 5: After about 6 months reconvene and evaluate. Is the ontology suited to its purpose ? Is it, in practice, usable ? Are we happy about its broad structure and content ? November 14, 2018

Step 6: Go public. Release ontology to community. Release the products of its instantiation. Invite broad community input and establish a mechanism for this (e.g. SourceForge). November 14, 2018

Step 7: Proselytize. Emphasize openness. Write a grant. Publish in a high profile journal. Engage new user groups. Emphasize openness. Write a grant. November 14, 2018

Step 8: Have fun! November 14, 2018

Take-home message Don’t reinvent—Use the power of combination and collaboration November 14, 2018

Improvements come in two forms Getting it right It is impossible to get it right the 1st (or 2nd, or 3rd, …) time. What we know about reality is continually growing Improve Collaborate and Learn The case studies from GO in the session on use-cases will illustrate both types of changes. November 14, 2018

Principles for Building Biomedical Ontologies Barry Smith http://ifomis.de November 14, 2018

Ontologies as Controlled Vocabularies expressing discoveries in the life sciences in a uniform way providing a uniform framework for managing annotation data deriving from different sources and with varying types and degrees of evidence November 14, 2018

Overview Following basic rules helps make better ontologies We will work through some examples of ontologies which do and not follow basic rules We will work through the principles-based treatment of relations in ontologies, to show how ontologies can become more reliable and more powerful November 14, 2018

Why do we need rules for good ontology? Ontologies must be intelligible both to humans (for annotation) and to machines (for reasoning and error-checking) Unintuitive rules for classification lead to entry errors (problematic links) Facilitate training of curators Overcome obstacles to alignment with other ontology and terminology systems Enhance harvesting of content through automatic reasoning systems e.g. menopause part-of aging, aging part-of death Database searching is one of the important kinds of reasoning we want to enable November 14, 2018

SNOMED-CT Top Level Substance Body Structure Specimen Context-Dependent Categories* Attribute Finding* Staging and Scales Organism Physical Object Events Environments and Geographic Locations Qualifier Value Special Concept* Pharmaceutical and Biological Products Social Context Disease Procedure Physical Force An illustration of the complexity involved in entering links in an ontology – entities can fall under radically different ontological categories November 14, 2018

Examples of Rules Don’t confuse entities with concepts Don’t confuse entities with ways of getting to know entities Don’t confuse entities with ways of talking about entities Don’t confuse entities with artifacts of your database representation ... An ontology should not change when the programming language changes November 14, 2018

First Rule: Univocity Terms (including those describing relations) should have the same meanings on every occasion of use. In other words, they should refer to the same kinds of entities in reality Problem example: ‘chromosome’ in Sequence Ontology and in Cell Component Ontology means different things Current solution: two distinct terms involved (qualified by respective namespace) November 14, 2018

Example of univocity problem in case of part_of relation (Old) Gene Ontology: ‘part_of’ = ‘may be part of’ flagellum part_of cell ‘part_of’ = ‘is at times part of’ replication fork part_of the nucleoplasm ‘part_of’ = ‘is included as a sub-list in’ November 14, 2018

Second Rule: Positivity Complements of classes are not themselves classes. Terms such as ‘non-mammal’ or ‘non-membrane’ do not designate genuine classes. There is no species called ‘non-rabbit’ November 14, 2018

Third Rule: Objectivity Which classes exist is not a function of our biological knowledge. Terms such as ‘unknown’ or ‘unclassified’ or ‘unlocalized’ do not designate biological natural kinds. There is no biological species: unknown rabbit. See discussion below. November 14, 2018

Fourth Rule: Single Inheritance No class in a classificatory hierarchy should have more than one is_a parent on the immediate higher level November 14, 2018

Rule of Single Inheritance no diamonds: B is_a1 C is_a2 A November 14, 2018

Problems with multiple inheritance B C is_a1 is_a2 A ‘is_a’ no longer univocal ‘is_a’ has two meanings – breaks the rule of univocity the multiple meanings are a reflection of a design choice (to keep GO simple by having multiple sorts of partitions brought together within the same framework) November 14, 2018

‘is_a’ is pressed into service to mean a variety of different things shortfalls from single inheritance are often clues to incorrect entry of terms and relations the resulting ambiguities make the rules for correct entry difficult to communicate to human curators November 14, 2018

is_a Overloading serves as obstacle to integration with neighboring ontologies The success of ontology alignment depends crucially on the degree to which basic ontological relations such as is_a and part_of can be relied on as having the same meanings in the different ontologies to be aligned. See “Relations in Biomedical Ontologys”, Genome Biology May 2005. November 14, 2018

Use of multiple inheritance The resultant mélange makes coherent integration across ontologies achievable (at best) only under the guidance of human beings with relevant biological knowledge How much should reasoning systems be forced to rely on human guidance? Cf. the issues with GO’s sensu terms. Scaling factor / use by non-biologists speak for maximal independence of human guidance. November 14, 2018

Fifth Rule: Intelligibility of Definitions The terms used in a definition should be simpler (more intelligible) than the term to be defined otherwise the definition provides no assistance to human understanding for machine processing November 14, 2018

To the degree that the above rules are not satisfied, error checking and ontology alignment will be achievable, at best, only with human intervention and via force majeure November 14, 2018

Some rules are Rules of Thumb The world of biomedical research is a world of difficult trade-offs The benefits of formal (logical and ontological) rigor need to be balanced Against the constraints of computer tractability, Against the needs of biomedical practitioners. BUT alignment and integration of biomedical information resources will be achieved only to the degree that such resources conform to these standard principles of classification and definition November 14, 2018

Current Best Practice: The Foundational Model of Anatomy Follows formal rules for definitions laid down by Aristotle. A definition is the specification of the essence (nature, invariant structure) shared by all the members of a class or natural kind. November 14, 2018

The Aristotelian Methodology Topmost nodes are the undefinable primitives. The definition of a class lower down in the hierarchy is provided by specifying the parent of the class together with the relevant differentia. Differentia tells us what marks out instances of the defined class within the wider parent class as in human == rational animal. November 14, 2018

FMA Examples Cell is an anatomical structure [topmost node] that consists of cytoplasm surrounded by a plasma membrane with or without a cell nucleus [differentia] November 14, 2018

The FMA regimentation Brings the advantage that each definition reflects the position in the hierarchy to which a defined term belongs. The position of a term within the hierarchy enriches its own definition by incorporating automatically the definitions of all the terms above it. The entire information content of the FMA’s term hierarchy can be translated very cleanly into a computer representation November 14, 2018

Definitions should be intelligible to both machines and humans Machines can cope with the full formal representation Humans need to use modularity Plasma membrane is a cell part [immediate parent] that surrounds the cytoplasm [differentia] November 14, 2018

Terms and relations should have clear definitions These tell us how the ontology relates to the world of biological instances, meaning the actual particulars in reality: actual cells, actual portions of cytoplasm, and so on… November 14, 2018

Sixth Rule: Basis in Reality When building or maintaining an ontology, always think carefully at how classes (types, kinds, species) relate to instances in reality November 14, 2018

Axioms governing instances Every class has at least one instance Every genus (parent class) has an instantiated species (differentia + genus) Each species (child class) has a smaller class of instances than its genus (parent class) To be presupposed when building, curating and using ontologies. November 14, 2018

Axioms governing Instances Distinct classes on the same level never share instances Distinct leaf classes within a classification never share instances November 14, 2018

species, genera instances substance organism animal mammal cat frog siamese leaf class frog instances November 14, 2018

Axioms Every genus (parent class) has at least two children UMLS Semantic Network November 14, 2018

Interoperability Ontologies should work together ways should be found to avoid redundancy in ontology building and to support reuse ontologies should be capable of being used by other ontologies (cumulation) November 14, 2018

Main obstacle to integration Current ontologies do not deal well with Time and Space and Instances (particulars) Our definitions should link the terms in the ontology to instances in spatio-temporal reality November 14, 2018

The problem of ontology alignment SNOMED MeSH UMLS NCIT HL7-RIM … None of these have clearly defined relations Still remain too much at the level of TERMINOLOGY Not based on a common set of rules Not based on a common set of relations There are some 100 different biomedical source vocabularies in the Unified Medical Language System (UMLS) prepared by the US National Library of Medicine. See: http://www.nlm.nih.gov/research/umls/ November 14, 2018

An example of an unclear definition A is_a B ‘A’ is more specific in meaning than ‘B’ unicorn is_a one-horned mammal HL7-RIM: Individual Allele is_a Act of Observation cancer documentation is_a cancer disease prevention is_a disease November 14, 2018

Benefits of well-defined relationships If the relations in an ontology are well-defined, then reasoning can cascade from one relational assertion (A R1 B) to the next (B R2 C). Relations used in ontologies thus far have not been well defined in this sense. Find all DNA binding proteins should also find all transcription factor proteins because Transcription factor is_a DNA binding protein November 14, 2018

How to define A is_a B A is_a B =def. A and B are names of universals (natural kinds, types) in reality all instances of A are as a matter of biological science also instances of B November 14, 2018

A standard definition of part_of A part_of B =def A composes (with one or more other physical units) some larger whole B This confuses relations between meanings or concepts with relations entities in reality From the UMLS Semantic Network November 14, 2018

Biomedical ontology integration / interoperability Will never be achieved through integration of meanings or concepts The problem is precisely that different user communities use different concepts What’s really needed is to have well-defined commonly used relationships See the Genome Biology paper “Relations in biomedical ontologies” : http://genomebiology.com/2005/6/5/R46 November 14, 2018

Idea: Move from associative relations between meanings to strictly defined relations between the entities themselves. The relations can then be used computationally in the way required November 14, 2018

Key idea: To define ontological relations For example: part_of, develops_from Definitions will enable computation It is not enough to look just at classes or types. We need also to take account of instances and time November 14, 2018

Kinds of relations Between classes: Between an instance and a class is_a, part_of, ... Between an instance and a class this explosion instance_of the class explosion Between instances: Mary’s heart part_of Mary November 14, 2018

Key In the following discussion: Classes are in upper case ‘A’ is the class Instances are in lower case ‘a’ is a particular instance November 14, 2018

Seventh Rule: Distinguish Universals and Instances A good ontology must distinguish clearly between universals (types, kinds, classes) and instances (tokens, individuals, particulars) November 14, 2018

Don’t forget instances when defining relations part_of as a relation between classes versus part_of as a relation between instances nucleus part_of cell your heart part_of you November 14, 2018

Part_of as a relation between classes is more problematic than is standardly supposed testis part_of human being ? heart part_of human being ? human being has_part human testis ? November 14, 2018

Analogous distinctions are required for nearly all foundational relations of ontologies and semantic networks: A causes B A is_located in B A is_adjacent_to B Reference to instances is necessary in defining mereotopological relations such as spatial occupation and spatial adjacency November 14, 2018

Why distinguish universals from instances? What holds on the level of instances may not hold on the level of universals nucleus adjacent_to cytoplasm Not: cytoplasm adjacent_to nucleus seminal vesicle adjacent_to urinary bladder Not: urinary bladder adjacent_to seminal vesicle November 14, 2018

part_of part_of must be time-indexed for spatial universals A part_of B is defined as: Given any instance a and any time t, If a is an instance of the universal A at t, then there is some instance b of the universal B such that a is an instance-level part_of b at t November 14, 2018

derives_from time instances ovum zygote derives_from sperm C1 C c1 at t1 C c at t instances time C' c' at t ovum zygote derives_from sperm November 14, 2018

transformation_of same instance time pre-RNA mature RNA adult child C1 c at t1 C c at t C1 time pre-RNA mature RNA adult child November 14, 2018

transformation_of C2 transformation_of C1 is defined as Given any instance c of C2 c was at some earlier time an instance of C1 November 14, 2018

embryological development c at t c at t1 C1 November 14, 2018

tumor development C1 C c at t c at t1 November 14, 2018 http://www.loni.ucla.edu/~thompson/HBM2000/tumor_volumes.jpg November 14, 2018

Definitions of the all-some form allow cascading inferences If A R1 B and B R2 C, then we know that every A stands in R1 to some B, but we know also that, whichever B this is, it can be plugged into the R2 relation, because R2 is defined for every B. November 14, 2018

Not only relations We can apply the same methodology to other top-level categories in ontology, e.g. anatomical structure process function (regulation, inhibition, suppression, co-factor ...) boundary, interior (contact, separation, continuity) tissue, membrane, sequence, cell See the discussions at the SOFG site: http://www.sofg.org/ November 14, 2018

Relations to describe topology of nucleic sequence features Based on the formal relationships between pairs of intervals in a 1-dimensional space. Uses the coincidence of edges and interiors Enables questions regarding the equality, overlap, disjointedness, containment and coverage of genomic features. Conventional operations in genomics are simplified Software no longer needs to know what kind of feature particular instances are November 14, 2018

An end of A intersects an end of B For features A & B An end of A intersects an end of B Interior of A intersects interior of B An end of A intersects interior of B Interior of A intersects an end of B A is disjoint from B False A meets B True A overlaps B A is inside B A contains B A covers B A is covered_by B A equals B November 14, 2018

disjoint b a An end of A does NOT intersect an end of B Interior of A does NOT intersect interior of B An end of A does NOT intersect interior of B Interior of A does NOT intersect an end of B November 14, 2018

meets a An end of A intersects an end of B b An end of A does NOT intersect interior of B Interior of A does NOT intersect an end of B Interior of A does NOT intersect interior of B November 14, 2018

overlaps Interior of A intersects interior of B a b An end of A intersects interior of B Interior of A intersects an end of B An end of A does NOT intersect an end of B November 14, 2018

inside a b An end of A intersects interior of B Interior of A intersects interior of B Interior of A does NOT intersect an end of B An end of A does NOT intersect an end of B November 14, 2018

contains a Interior of A intersects an end of B Interior of A intersects interior of B b An end of A does NOT intersect an end of B An end of A does NOT intersect interior of B November 14, 2018

covers Interior of A intersects interior of B a An end of A intersects an end of B Interior of A intersects an end of B b An end of A does NOT intersect interior of B November 14, 2018

covered_by Interior of A intersects interior of B a An end of A intersects an end of B An end of A intersects interior of B b Interior of A does NOT intersect an end of B November 14, 2018

equals a b An end of A intersects an end of B Interior of A intersects interior of B An end of A does NOT intersect an interior of B Interior of A does NOT intersect an end of B November 14, 2018

The Rules Univocity: Terms should have the same meanings on every occasion of use Positivity: Terms such as ‘non-mammal’ or ‘non-membrane’ do not designate genuine classes. Objectivity: Terms such as ‘unknown’ or ‘unclassified’ or ‘unlocalized’ do not designate biological natural kinds. Single Inheritance: No class in a classification hierarchy should have more than one is_a parent on the immediate higher level Intelligibility of Definitions: The terms used in a definition should be simpler (more intelligible) than the term to be defined Basis in Reality: When building or maintaining an ontology, always think carefully at how classes relate to instances in reality Distinguish Universals and Instances November 14, 2018

What we have argued for: A methodology which enforces clear, coherent definitions This promotes quality assurance intent is not hard-coded into software Meaning of relationships is defined, not inferred Guarantees automatic reasoning across ontologies and across data at different granularities November 14, 2018

Principles for Building Biomedical Ontologies Rama Balakrishnan and David Hill http://www.geneontology.org November 14, 2018

How has GO dealt with some specific aspects of ontology development? Univocity Positivity Objectivity Definitions Formal definitions Written definitions Ontology Alignment November 14, 2018

The Challenge of Univocity: People call the same thing by different names Tactition Taction Tactile sense ? November 14, 2018

Univocity: GO uses 1 term and many characterized synonyms Taction Tactition Tactile sense perception of touch ; GO:0050975 November 14, 2018

The Challenge of Univocity: People use the same words to describe different things = bud initiation = bud initiation = bud initiation November 14, 2018

Bud initiation? How is a computer to know? November 14, 2018

Univocity: GO adds “sensu” descriptors to discriminate among organisms = bud initiation sensu Metazoa = bud initiation sensu Saccharomyces = bud initiation sensu Viridiplantae November 14, 2018

The Challenge of Positivity Some organelles are membrane-bound. A centrosome is not a membrane bound organelle, but it still may be considered an organelle. November 14, 2018

membrane-bound organelle The Challenge of Positivity: Sometimes absence is a distinction in a Biologist’s mind non-membrane-bound organelle GO:0043228 membrane-bound organelle GO:0043227 November 14, 2018

Positivity Note the logical difference between “non-membrane-bound organelle” and “not a membrane-bound organelle” The latter includes everything that is not a membrane bound organelle! November 14, 2018

about a gene product with The Challenge of Objectivity: Database users want to know if we don’t know anything (Exhaustiveness with respect to knowledge) We don’t know anything about the ligand that binds this type of GPCR We don’t know anything about a gene product with respect to these November 14, 2018

Objectivity How can we use GO to annotate gene products when we know that we don’t have any information about them? Currently GO has terms in each ontology to describe unknown An alternative might be to annotate genes to root nodes and use an evidence code to describe that we have no data. Similar strategies could be used for things like receptors where the ligand is unknown. November 14, 2018

GPCRs with unknown ligands We could annotate to this November 14, 2018

GO Definitions A definition written by a biologist: necessary & sufficient conditions written definition (not computable) Graph structure: necessary conditions formal (computable) November 14, 2018

Relationships and definitions The set of necessary conditions is determined by the graph This can be considered a partial definition Important considerations: Placement in the graph- selecting parents Appropriate relationships to different parents True path violation November 14, 2018

Placement in the graph Example- Proteasome complex November 14, 2018 Mulitple parents for one term; different relationships with different parents. Example- Proteasome complex November 14, 2018

The importance of relationships Cyclin dependent protein kinase Complex has a catalytic and a regulatory subunit How do we represent these activities (function) in the ontology? Do we need a new relationship type (regulates)? Molecular_function Catalytic activity Enzyme regulator activity protein kinase activity Protein kinase regulator activity protein Ser/Thr kinase activity Cyclin dependent protein kinase activity Cyclin dependent protein kinase regulator activity November 14, 2018

True path violation What is it? ..”the pathway from a child term all the way up to its top-level parent(s) must always be true". nucleus Part_of relationship chromosome Is_a relationship Mitochondrial chromosome November 14, 2018

True path violation What is it? ..”the pathway from a child term all the way up to its top-level parent(s) must always be true". nucleus chromosome Part_of relationship Is_a relationships Nuclear chromosome Mitochondrial chromosome November 14, 2018

Triplet_codon amino acid adaptor activity The Importance of synonyms for utility: How do we represent the function of tRNA? Biologically, what does the tRNA do? Identifies the codon and inserts the amino acid in the growing polypeptide Molecular_function Triplet_codon amino acid adaptor activity GO Definition: Mediates the insertion of an amino acid at the correct point in the sequence of a nascent polypeptide chain during protein synthesis. Synonym: tRNA November 14, 2018

GO textual definitions: Related GO terms have similarly structured (normalized) definitions November 14, 2018

Structured definitions contain both genus and differentiae Essence = Genus + Differentiae neuron cell differentiation = Genus: differentiation (processes whereby a relatively unspecialized cell acquires the specialized features of..) Differentiae: acquires features of a neuron November 14, 2018

Ontology alignment One of the current goals of GO is to align: Cell Types in GO Cell Types in the Cell Ontology with cone cell fate commitment retinal_cone_cell keratinocyte differentiation keratinocyte adipocyte differentiation fat_cell dendritic cell activation dendritic_cell (as of 4/20/05 this was the status) lymphocyte proliferation lymphocyte T-cell homeostasis T_lymphocyte garland cell differentiation garland_cell heterocyst cell differentiation heterocyst November 14, 2018

Alignment of the Two Ontologies will permit the generation of consistent and complete definitions GO + id: CL:0000062 name: osteoblast def: "A bone-forming cell which secretes an extracellular matrix. Hydroxyapatite crystals are then deposited into the matrix to form bone." [MESH:A.11.329.629] is_a: CL:0000055 relationship: develops_from CL:0000008 relationship: develops_from CL:0000375 Cell type = Osteoblast differentiation: Processes whereby an osteoprogenitor cell or a cranial neural crest cell acquires the specialized features of an osteoblast, a bone-forming cell which secretes extracellular matrix. New Definition November 14, 2018

Alignment of the Two Ontologies will permit the generation of consistent and complete definitions id: GO:0001649 name: osteoblast differentiation synonym: osteoblast cell differentiation genus: differentiation GO:0030154 (differentiation) differentium: acquires_features_of CL:0000062 (osteoblast) definition (text): Processes whereby a relatively unspecialized cell acquires the specialized features of an osteoblast, the mesodermal cell that gives rise to bone Formal definitions with necessary and sufficient conditions, in both human readable and computer readable forms November 14, 2018

Other Ontologies that can be aligned with GO Chemical ontologies 3,4-dihydroxy-2-butanone-4-phosphate synthase activity Anatomy ontologies metanephros development GO itself mitochondrial inner membrane peptidase activity November 14, 2018

But Eventually… November 14, 2018

Building Ontology Improve Collaborate and Learn November 14, 2018

Debate of critical issues Barry Smith, Michael Ashburner, and Mark Musen November 14, 2018

Bloggers and other online groups (eg. del. icio Bloggers and other online groups (eg. del.icio.us, Flickr [online photo archive], Technorati) have been self-categorizing or 'tagging' web sites and their content using user-defined words and phrases and not an expertly curated vocabulary or ontology. The end result is that a vast amount of content has been indexed using a rich vocabulary of tags (to date, technorati has over 1.2 billion links tagged with 1.2 million tags). Whilst this certainly lacks the formal consistency that would be obtained with curated annotation against a standard vocabulary, the quantity of content being categorized far exceeds what could be done by a group of annotators and perhaps is richer because the tags are defined by the users and creators of that content, not by a third party interpreting the material after the fact. Given the ever increasing quantity of scientific data, the proliferation of online publishing, etc., could scientists tagging their own data with their own terms be the way to go? November 14, 2018

How can you recruit and train people, to the needed level of expertise in both logic and biology, given that without a sufficient number of competent personnel the ontology cannot be maintained? November 14, 2018

Assuming that we use good practice, how do we then get biologists to employ these ontologies? Phrased a different way, will an ontology, such as the GO, change how users describe biology? November 14, 2018

What is the difference between function and process? November 14, 2018

Will there will ever be (or should one hope for) a core mega-ontology, to which all the other ontologies should or could link, representing biology in its entirety? November 14, 2018

We hope this afternoon was useful and informative. Thank You! We hope this afternoon was useful and informative. November 14, 2018

What do YOU call an ontology? Controlled vocabularies A simple list of terms For example, EpoDB: gene names and families, developmental stages, cell types, tissue types, experiment names, and chemical factors November 14, 2018

What do YOU call an ontology? Pure subsumption hierarchies single ‘is_a’ relationship For example, eVoc for attributes of cDNA libraries: Anatomical system, cell type, development stage, experimental technique, microarray platform, pathology, pooling strategy, tissue preparation, treatment November 14, 2018

Charcot-Marie tooth disease eVOC is_a hierarchy Pathology Genetic disorder Infectious disorder Charcot-Marie tooth disease Denys-drash viral bacterial AIDS cytomegalovirus November 14, 2018

What is it YOU call an ontology? Data Model BioPax: a specification for data exchange of biological (metabolic) processes Hybrids Gene Ontology: Mix of subsumption (is_a), part_of, and derives_from relationships November 14, 2018

What do YOU call an ontology? Suite NCI Thesaurus Knowledgebases PharmGKB Reactome IMGT (Immunogenetics] November 14, 2018